Statistical methods for analyzing high-throughput genotype data Rapid technological progress makes high-throughput genotyping of thousands of SNPs feasible in large epidemiologic studies. Technologies to sequence an entire human genome for affordable cost is expected in near future. New statistical methods that are able to manage and analyze this sort of large scale data have not progressed as rapidly, however. This renewal application requests support to continue statistical methodological developments in analyzing large scale genetic data sets. We propose to use a variety of large genetic data sets to test the newly developed statistical methods. Specific aims include 1) Develop statistical methods to detect rare genetic variants using whole genome scan or sequence data. We will develop a variety of designs to cluster rare risk haplotypes and then perform association analysis with these risk haplotypes as a group in candidate gene association studies. 2) Develop statistical association methods that control for population stratification using whole genome data. 3) Develop statistical methods to jointly model admixture mapping and association in order to search for potential causal variants contributing to the admixture mapping signals. 4) Develop corresponding software that will be made available in the S.A.G.E. (Statistical Analysis for Genetic Epidemiology) program package which will be widely distributed. We will collaborate with laboratory-based investigators to obtain appropriate data sets, including our hypertension and obesity related data, and apply new analytic methods to this crucial practical problem in genetic epidemiology. PUBLIC HEALTH RELEVANCE: This is a continuation of research with the aims of developing novel statistical methods for detecting genetic variants underlying common diseases. We will develop the statistical methods of detecting rare variants, controlling population stratification, performing the joint analysis of admixture mapping and association and developing new software.